The objective of this study is to improve server speed using neural network algorithms compared with random forest techniques to increase accuracy using machine learning. Option posts by players and upvoting-based prediction and suggestions are also used. Research focuses on two groups: group 1's random forest and neural network algorithms, and group 2's Template matching method. There are twenty samples in each category. Given that the computation has a significant value of 80%, the estimated sample size is 40 utilizing a G power of 80%. The accuracy from the neural network algorithms is 87.96, it seems superior to Random forest algorithms is 80.94% respectively, and has an important difference p = 0.001In the SPSS statistical analysis, (p < 0.05,). The results show that the neural network approach greatly outperforms the random forest algorithm for predicting option postings by players as well as suggestions and predictions based on upvotes.